Context-based intelligent recommendation by code reuse for smart decision support and cognitive adaptive systems

PurposeThis paper aims to use the concept of machine learning to enable people and machines to interact more certainly to extend and expand human expertise and cognition.Design/methodology/approachIntelligent code reuse recommendations based on code big data analysis, mining and learning can effectively improve the efficiency and quality of software reuse, including common code units in a specific field and common code units that are not related to the field.FindingsFocusing on the topic of context-based intelligent code reuse recommendation, this paper expounds the research work in two aspects mainly in practical applications of smart decision support and cognitive adaptive systems: code reuse recommendation based on template mining and code reuse recommendation based on deep learning.Originality/valueOn this basis, the future development direction of intelligent code reuse recommendation based on context has prospected.

Feature Reuse Across Software Releases During Software Evolution1

Software evolution relies on storing component versions along with delta-changes in a repository of a version control tool such a centralized CVS in old days, or decentralized Git today. Code implementing various software features (e.g., requirements) often spreads over multiple software components, and across multiple versions of those components. Not having a clear picture of feature implementation and evolution may hinder software reuse which most often is concerned with feature reuse across system releases, and components are just means to that end. Much research on feature location shows how important and difficult is to find feature-related code buried in program components post mortem. We propose to avoid creating the problem in the first place, by explicating feature-related code in component versions at the time of their implementation. To do that, we complement traditional version control approach with generative mechanisms. We describe salient features of such an approach realized in ART (Adaptive Reuse Technology, http://art-processor.org), and explain its role in easing comprehending software evolution and feature reuse. Advanced commercial version control tools make a step towards easing the evolution problems addressed in this paper. Our approach is an alternative way of addressing the same problem on quite a different ground.

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.